Case study · Flagship build
Raydar.
An AI-assisted commerce intelligence system that turns fragmented trend, market, and product evidence into structured, explainable decisions — built for my own digital-design business.
The problem
More information wasn't the answer.
A digital-design business generates a constant stream of market signals, competitor moves, and performance data. The real constraint was never a shortage of information — it was turning those scattered inputs into one consistent decision about what deserved production time.
The system
A decision system, not a trend dashboard.
Rather than another analytics dashboard, Raydar structures evidence, applies deterministic scoring logic, explains the reasoning behind every score, and carries an approved opportunity through a complete workflow — from research through launch and performance review.
How it scores
Eight weighted dimensions, explained every time.
Every opportunity is scored the same way, so patterns are comparable and the reasoning behind a recommendation is never a black box.
How much real buyer demand the evidence supports.
Whether the signal is accelerating or fading.
Can this become a repeatable product line, not a one-off.
Room for variation without diluting the core idea.
How well it fits the shop's actual capabilities.
Room in the market versus how saturated it already is.
How strong and independent the supporting evidence is.
A penalty applied when a space is already crowded.
Human-in-the-loop
Code calculates. AI interprets. Humans decide.
- Code calculates. Numerical scoring is handled by deterministic code, not an LLM — reproducible and explainable every time.
- AI interprets. AI operates at the interpretation layer: summarizing evidence, identifying patterns, and helping explain why an opportunity may matter.
- Humans decide. The system organizes, compares, scores, and recommends — a person makes the final call.
"The hardest part of AI product design is deciding where AI should stop. Useful automation isn't measured by how much human judgment it removes."
Public vs. private
What's public, what stays private.
Raydar runs in two modes on the same underlying product — the public demo restricts what's visible, it doesn't diverge into a separate app.
Public demo
The interface and product logic you can click into right now, running on synthetic, anonymized data. No live LLM calls, nothing persisted.
Private operating system
The full version I actually run day-to-day — real evidence, real sources, and real shop data. Not exposed publicly.
Source code
The public demo's code is open on GitHub, built with Next.js, TypeScript, React, and Recharts, deployed on Vercel.
Built & owned by
Rachel Beecham
Systems-minded builder combining business ownership with hands-on AI software engineering. Want to talk about how this was built?